Computational video editing for fictional works tends to fall in one of two lines of research: transcript-based approaches [1]}, [2]}, [3]}, [4]} and learning-based approaches [5]}. Leake et. al. [1]} generates edited video sequences using a standard film script and multiple takes of the scene, but their work is specific to dialogue-driven scenes. Two similar concepts, Write-A-Video [2]} and QuickCut [4]}, generate video montages using a combination of text and a video library. Learning-based approaches have seen success in recent years, notably in Learning to Cut [5]}, which proposes a method to rank realistic cuts via contrastive learning [10]}. The MovieCuts dataset [11]} includes match cuts as a subtype, though it is by far the smallest category and does not distinguish between kinds of match cuts. In contrast, we release a data set of 20k pairs that differentiate between frame and motion cuts, with the goal of finding these pairs from shots throughout the film instead of detecting existing cuts. Our work advances learning-based computational video editing by introducing a method to generate and then rank proposed pairs of match cuts without
fixed rules or transcripts.
